I have been carefully watching for stories about the growing influence of technology in our lives, and sharing links with the DigiDig team via Toni Muzi Falcone. We discussed turning these into a weekly digest or DigiDigest (I truly hope that puns and weak humor attempts are not lost in translation; otherwise my writing tenure will be short here).
So, without further adieu, I list three very relevant stories.
Mother Nature’s Network – How Algorithms Influence us Every Day
Mashable – Online Shopping Algorithms have us in a Decision Rut
Lance Ulanoff sees a “fundamental flaw in the technology designed to serve up things we might like. They are based entirely on past choices and activities and leave zero room for improvisation and unpredictability.” He bemoans the loss of serendipity in shopping recommendations, for example. It’s a timely topic in the US, as Cyber Monday was yesterday and the holidays are looming.
Lance writes: “If we continue to follow the choices made for us on social, services, subscription and retail sites, we will all soon be living a very vanilla life. Our friends will be the same kinds of people, our social feeds will offer just one point of view and our gift-giving will surprise no one.It is time to stand up and say, ‘You don’t know me.'”
Seems Lance would disagree with Cory’s view on the technology’s benefits.
Quanta Magazine – How to Force our Machines to Play Fair
Quanta writer Kevin Hartnett interviews author and Microsoft Distinguished Scientists Cynthia Dwork, who pioneered ideas behind “differential privacy.” She is now taking on fairness in algorithm design.
Cynthia says: “algorithms… could affect individuals’ options in life.. to determine what kind of advertisements to show people. We may not be used to thinking of ads as great determiners of our options in life. But what people get exposed to has an impact on them.”
She explores individual vs. group fairness and introduces the idea of “fair affirmative action.” Dwork would love to find a metric or way to ensure that “similar people [get] treated similarly,” but concludes that it is a thorny problem that people must first come to terms with before training computers to make these judgments.